Predictors of packed red cell transfusion after isolated primary coronary artery bypass grafting – The experience of a single cardiac center: A prospective observational study
Bibliographic record
Abstract
BACKGROUND: Preoperative patients' characteristics can predict the need for perioperative blood component transfusion in cardiac surgical operations. The aim of this prospective observational study is to identify perioperative patient characteristics predicting the need for allogeneic packed red blood cell (PRBC) transfusion in isolated primary coronary artery bypass grafting (CABG) operations. PATIENTS AND METHODS: 105 patients undergoing isolated, first-time CABG were reviewed for their preoperative variables and followed for intraoperative and postoperative data. Patients were 97 males and 8 females, with mean age 58.28 +/- 10.97 years. Regression logistic analysis was used for identifying the strongest perioperative predictors of PRBC transfusion. RESULTS: PRBC transfusion was used in 71 patients (67.6%); 35 patients (33.3%) needed > 2 units and 14 (13.3%) of these needed > 4 units. Univariate analysis identified female gender, age > 65 years, body weight < or = 70 Kg, BSA < or = 1.75 m(2), BMI < or = 25, preoperative hemoglobin < or = 13 gm/dL, preoperative hematocrit < or = 40%, serum creatinine > 100 micromol/L, Euro SCORE (standard/logistic) > 2, use of CPB, radial artery use, higher number of distal anastomoses, and postoperative chest tube drainage > 1000 mL as significant predictors. The strongest predictors using multivariate analysis were CPB use, hematocrit, body weight, and serum creatinine. CONCLUSION: The predictors of PRBC transfusion after primary isolated CABG are use of CPB, hematocrit < or = 40%, weight < or = 70 Kg, and serum creatinine > 100 micromol/L. This leads to better utilization of blood bank resources and cost-efficient targeted use of expensive blood conservation modalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".